Browsing by Author "Izci, Elif"

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Citation - WoS: 8
Ecg Arrhythmia Detection With Deep Learning
(IEEE, 2020) Izci, Elif; Degirmenci, Murside; Ozdemir, Mehmet Akif; Akan, Aydin
Arrhythmia is any irregularity of heart rate that cause an abnormality in your heart rhythm. Manual analysis of Electrocardiogram (ECG) signal is not enough for quickly identify abnormalities in the heart rhythm. This paper proposes a deep learning approach for detection of five different arrhythmia types based on 2D convolutional neural networks (CNN) architecture. ECG signals were obtained from MIT-BIll arrhythmia database. For CNN architecture, each ECG signal was segmented into heartbeats, then each heartbeat was transformed into 2D grayscale heartbeat image. 2D CNN model was used due to success of image recognition. The proposed model result demonstrate that CNN and ECG image formation give highest result when classified different types of ECG arrhythmic signals.
Citation - WoS: 3
Citation - Scopus: 2
An Eeg and Machine Learning Based Method for the Detection of Major Depressive Disorder
(IEEE, 2021) Izci, Elif; Ozdemir, Mehmet Akif; Akan, Aydin; Ozcoban, Mehmet Akif; Arikan, Mehmet Kemal
Major depressive disorder (MDD) is a common mood disorder encountered worldwide. Early diagnosis has great importance to prevent the negative effects on the person. The aim of this study is to develop an objective method to differentiate MDD patients from healthy controls. Electroencephalography (EEG) signals taken from 16 MDD patients and 16 healthy subjects are analyzed according to the regions of the brain, and time-domain, frequency-domain, and nonlinear features were extracted. The feature sets are classified using five different classification algorithms. As a result of the study, a classification accuracy of 89.5% was yielded using the Bagging classifier with 7 features calculated from the central EEG channels.